A Model-based Vehicle Segmentation Method for Tracking

Xuefeng Song


Abstract

Our goal is to detect and track moving vehicles on a road observed from cameras placed on poles or buildings. Inter-vehicle occlusion is significant under these conditions and traditional blob tracking methods will be unable to separate the vehicles in the merged blobs. We use vehicle shape models, in addition to camera calibration and ground plane knowledge, to detect, track and classify moving vehicles in presence of occlusion. We use a 2-stage approach. In the first stage, hypothesis for vehicle types, positions and orientations are formed by a coarse search, which is then refined by a data driven Markov Chain Monte Carlo (DDMCMC) process. We show results and evaluations on some real urban traffic video sequence using three types of vehicle models.


Maintained by Changki Min